Overview

Dataset statistics

Number of variables27
Number of observations5032
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory216.0 B

Variable types

Numeric17
Categorical10

Alerts

PolicyholderName has a high cardinality: 5031 distinct values High cardinality
PlanName has a high cardinality: 5032 distinct values High cardinality
PolicyholderID is highly correlated with IncomeofPolicyholderperannum and 9 other fieldsHigh correlation
IncomeofPolicyholderperannum is highly correlated with PolicyholderID and 2 other fieldsHigh correlation
Planenrolledyear is highly correlated with PolicyholderID and 12 other fieldsHigh correlation
PolicyTerminyears is highly correlated with PolicyholderID and 9 other fieldsHigh correlation
Sumassured is highly correlated with PolicyholderID and 8 other fieldsHigh correlation
YearofMaturity is highly correlated with PolicyholderID and 10 other fieldsHigh correlation
YearlyPremium is highly correlated with PolicyholderID and 8 other fieldsHigh correlation
MonthlyPremium is highly correlated with PolicyholderID and 8 other fieldsHigh correlation
NumberofPremiumspaidinyears is highly correlated with PolicyholderID and 12 other fieldsHigh correlation
NumberofPremiumspaidtilldateinmonths is highly correlated with PolicyholderID and 12 other fieldsHigh correlation
PremiumPaidtilldate is highly correlated with PolicyholderID and 11 other fieldsHigh correlation
latepayment0to3months is highly correlated with Planenrolledyear and 4 other fieldsHigh correlation
Totaldelayedmonths is highly correlated with Planenrolledyear and 7 other fieldsHigh correlation
RiskScore is highly correlated with Planenrolledyear and 6 other fieldsHigh correlation
Default is highly correlated with Planenrolledyear and 5 other fieldsHigh correlation
latepayment6to9months is highly correlated with TotaldelayedmonthsHigh correlation
latepayment9to12months is highly correlated with Morethan12monthsdelay and 1 other fieldsHigh correlation
Morethan12monthsdelay is highly correlated with latepayment9to12months and 1 other fieldsHigh correlation
PolicyholderAgeinyears is highly correlated with MaritalStatus and 1 other fieldsHigh correlation
MaritalStatus is highly correlated with PolicyholderAgeinyearsHigh correlation
Nominee is highly correlated with PolicyholderAgeinyearsHigh correlation
Accomodation is highly correlated with IncomeofPolicyholderperannumHigh correlation
PolicyholderID is uniformly distributed Uniform
PolicyholderName is uniformly distributed Uniform
PlanName is uniformly distributed Uniform
PolicyholderID has unique values Unique
PlanName has unique values Unique
latepayment6to9months has 4781 (95.0%) zeros Zeros

Reproduction

Analysis started2023-03-19 14:57:57.793570
Analysis finished2023-03-19 14:58:56.665340
Duration58.87 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

PolicyholderID
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct5032
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2516.876391
Minimum1
Maximum5033
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:28:56.784882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

PolicyholderName
Categorical

HIGH CARDINALITY
UNIFORM

Distinct5031
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
Irfan Khan
 
2
Sarat Sharma
 
1
V Arumugam
 
1
Sathyavathi Putta
 
1
Vijay Mech
 
1
Other values (5026)
5026 

Length

Max length43
Median length31
Mean length13.80584261
Min length1

Characters and Unicode

Total characters69471
Distinct characters114
Distinct categories16 ?
Distinct scripts8 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5030 ?
Unique (%)> 99.9%

Sample

1st rowSarat Sharma
2nd rowSandeep Balasubramanian
3rd rowPrem Kumar
4th rowSajal Toshniwal
5th rowChandrakumar

Common Values

ValueCountFrequency (%)
Irfan Khan2
 
< 0.1%
Sarat Sharma1
 
< 0.1%
V Arumugam1
 
< 0.1%
Sathyavathi Putta1
 
< 0.1%
Vijay Mech1
 
< 0.1%
Sudhakharan Mohanram1
 
< 0.1%
Shiwalika Gupta1
 
< 0.1%
Ravichandran Arumugam1
 
< 0.1%
Mohammed Junaid1
 
< 0.1%
Manoj Veer1
 
< 0.1%
Other values (5021)5021
99.8%

Length

2023-03-19T20:28:57.076709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kumar220
 
2.3%
s84
 
0.9%
raj70
 
0.7%
r54
 
0.6%
m54
 
0.6%
k54
 
0.6%
suresh50
 
0.5%
jain49
 
0.5%
srinivasan48
 
0.5%
ramesh47
 
0.5%
Other values (4282)9011
92.5%

Most occurring characters

ValueCountFrequency (%)
a13461
19.4%
n5538
 
8.0%
4760
 
6.9%
r4308
 
6.2%
h4105
 
5.9%
i3989
 
5.7%
e2990
 
4.3%
u2422
 
3.5%
m2297
 
3.3%
s2270
 
3.3%
Other values (104)23331
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter54619
78.6%
Uppercase Letter9842
 
14.2%
Space Separator4760
 
6.9%
Other Punctuation148
 
0.2%
Decimal Number27
 
< 0.1%
Other Letter19
 
< 0.1%
Open Punctuation12
 
< 0.1%
Close Punctuation12
 
< 0.1%
Connector Punctuation8
 
< 0.1%
Nonspacing Mark6
 
< 0.1%
Other values (6)18
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a13461
24.6%
n5538
10.1%
r4308
 
7.9%
h4105
 
7.5%
i3989
 
7.3%
e2990
 
5.5%
u2422
 
4.4%
m2297
 
4.2%
s2270
 
4.2%
t1849
 
3.4%
Other values (29)11390
20.9%
Uppercase Letter
ValueCountFrequency (%)
S1794
18.2%
R1072
10.9%
K956
9.7%
M789
8.0%
A753
 
7.7%
P620
 
6.3%
V603
 
6.1%
J463
 
4.7%
N429
 
4.4%
B424
 
4.3%
Other values (19)1939
19.7%
Other Letter
ValueCountFrequency (%)
3
15.8%
ي2
 
10.5%
2
 
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (5)5
26.3%
Decimal Number
ValueCountFrequency (%)
27
25.9%
16
22.2%
53
11.1%
83
11.1%
03
11.1%
62
 
7.4%
71
 
3.7%
91
 
3.7%
41
 
3.7%
Other Punctuation
ValueCountFrequency (%)
.126
85.1%
'9
 
6.1%
/8
 
5.4%
@2
 
1.4%
#1
 
0.7%
&1
 
0.7%
,1
 
0.7%
Other Symbol
ValueCountFrequency (%)
🧿1
33.3%
1
33.3%
1
33.3%
Nonspacing Mark
ValueCountFrequency (%)
4
66.7%
2
33.3%
Currency Symbol
ValueCountFrequency (%)
$2
50.0%
2
50.0%
Space Separator
ValueCountFrequency (%)
4760
100.0%
Open Punctuation
ValueCountFrequency (%)
(12
100.0%
Close Punctuation
ValueCountFrequency (%)
)12
100.0%
Connector Punctuation
ValueCountFrequency (%)
_8
100.0%
Control
ValueCountFrequency (%)
5
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%
Private Use
ValueCountFrequency (%)
1
100.0%
Spacing Mark
ValueCountFrequency (%)
ி1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin64449
92.8%
Common4983
 
7.2%
Tamil16
 
< 0.1%
Cyrillic7
 
< 0.1%
Katakana6
 
< 0.1%
Greek5
 
< 0.1%
Arabic4
 
< 0.1%
Unknown1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a13461
20.9%
n5538
 
8.6%
r4308
 
6.7%
h4105
 
6.4%
i3989
 
6.2%
e2990
 
4.6%
u2422
 
3.8%
m2297
 
3.6%
s2270
 
3.5%
t1849
 
2.9%
Other values (50)21220
32.9%
Common
ValueCountFrequency (%)
4760
95.5%
.126
 
2.5%
(12
 
0.2%
)12
 
0.2%
'9
 
0.2%
_8
 
0.2%
/8
 
0.2%
27
 
0.1%
16
 
0.1%
5
 
0.1%
Other values (17)30
 
0.6%
Tamil
ValueCountFrequency (%)
4
25.0%
3
18.8%
2
12.5%
2
12.5%
1
 
6.2%
1
 
6.2%
ி1
 
6.2%
1
 
6.2%
1
 
6.2%
Cyrillic
ValueCountFrequency (%)
а2
28.6%
Я1
14.3%
и1
14.3%
м1
14.3%
ѕ1
14.3%
н1
14.3%
Katakana
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Arabic
ValueCountFrequency (%)
ي2
50.0%
ج1
25.0%
ل1
25.0%
Greek
ValueCountFrequency (%)
Λ3
60.0%
ε2
40.0%
Unknown
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII69414
99.9%
None18
 
< 0.1%
Tamil16
 
< 0.1%
Cyrillic7
 
< 0.1%
Katakana6
 
< 0.1%
Arabic4
 
< 0.1%
Currency Symbols2
 
< 0.1%
Misc Symbols2
 
< 0.1%
PUA1
 
< 0.1%
IPA Ext1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a13461
19.4%
n5538
 
8.0%
4760
 
6.9%
r4308
 
6.2%
h4105
 
5.9%
i3989
 
5.7%
e2990
 
4.3%
u2422
 
3.5%
m2297
 
3.3%
s2270
 
3.3%
Other values (65)23274
33.5%
None
ValueCountFrequency (%)
ö4
22.2%
Λ3
16.7%
ŋ2
11.1%
ε2
11.1%
ı2
11.1%
ē1
 
5.6%
ý1
 
5.6%
🧿1
 
5.6%
Ɩ1
 
5.6%
ơ1
 
5.6%
Tamil
ValueCountFrequency (%)
4
25.0%
3
18.8%
2
12.5%
2
12.5%
1
 
6.2%
1
 
6.2%
ி1
 
6.2%
1
 
6.2%
1
 
6.2%
Cyrillic
ValueCountFrequency (%)
а2
28.6%
Я1
14.3%
и1
14.3%
м1
14.3%
ѕ1
14.3%
н1
14.3%
Arabic
ValueCountFrequency (%)
ي2
50.0%
ج1
25.0%
ل1
25.0%
Currency Symbols
ValueCountFrequency (%)
2
100.0%
Katakana
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Misc Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
PUA
ValueCountFrequency (%)
1
100.0%
IPA Ext
ValueCountFrequency (%)
ʂ1
100.0%

PolicyholderAgeinyears
Real number (ℝ≥0)

HIGH CORRELATION

Distinct643
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.03196855
Minimum28.03835616
Maximum92.03287671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:28:57.193543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum28.03835616
5-th percentile33.01643836
Q141.02945205
median51.03835616
Q362.02465753
95-th percentile75.03410959
Maximum92.03287671
Range63.99452055
Interquartile range (IQR)20.99520548

Descriptive statistics

Standard deviation13.31552782
Coefficient of variation (CV)0.2559105141
Kurtosis-0.6038824772
Mean52.03196855
Median Absolute Deviation (MAD)10.02465753
Skewness0.33939566
Sum261824.8658
Variance177.3032812
MonotonicityNot monotonic
2023-03-19T20:28:57.371416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35192
 
3.8%
3423
 
0.5%
46.0328767122
 
0.4%
54.0328767120
 
0.4%
56.0246575320
 
0.4%
45.0136986320
 
0.4%
61.0301369918
 
0.4%
52.0191780818
 
0.4%
49.0246575318
 
0.4%
47.0328767117
 
0.3%
Other values (633)4664
92.7%
ValueCountFrequency (%)
28.038356161
 
< 0.1%
30.013698638
0.2%
30.016438369
0.2%
30.019178084
0.1%
30.021917817
0.1%
30.024657537
0.1%
30.027397265
0.1%
30.030136997
0.1%
30.032876716
0.1%
30.035616444
0.1%
ValueCountFrequency (%)
92.032876711
< 0.1%
91.032876711
< 0.1%
91.027397262
< 0.1%
91.021917811
< 0.1%
91.016438361
< 0.1%
90.041095892
< 0.1%
90.038356161
< 0.1%
90.024657532
< 0.1%
90.013698631
< 0.1%
89.041095892
< 0.1%

IncomeofPolicyholderperannum
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2950
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266466.4789
Minimum75050
Maximum3090130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:28:57.534702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum75050
5-th percentile87301
Q1132150
median198530
Q3301845
95-th percentile667110
Maximum3090130
Range3015080
Interquartile range (IQR)169695

Descriptive statistics

Standard deviation223933.3365
Coefficient of variation (CV)0.8403808892
Kurtosis23.84377723
Mean266466.4789
Median Absolute Deviation (MAD)77510
Skewness3.680713781
Sum1340859322
Variance5.01461392 × 1010
MonotonicityNot monotonic
2023-03-19T20:28:57.691008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
637640134
 
2.7%
73762092
 
1.8%
43758068
 
1.4%
53747065
 
1.3%
43762057
 
1.1%
52511014
 
0.3%
18015014
 
0.3%
52213014
 
0.3%
15009013
 
0.3%
22644012
 
0.2%
Other values (2940)4549
90.4%
ValueCountFrequency (%)
750501
 
< 0.1%
750603
 
0.1%
750706
0.1%
750803
 
0.1%
750905
0.1%
751003
 
0.1%
751103
 
0.1%
751205
0.1%
751309
0.2%
751404
0.1%
ValueCountFrequency (%)
30901301
< 0.1%
26758401
< 0.1%
24900502
< 0.1%
22465401
< 0.1%
20512401
< 0.1%
19200901
< 0.1%
19200301
< 0.1%
18751301
< 0.1%
18750901
< 0.1%
18720501
< 0.1%

MaritalStatus
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
Married
4420 
Unmarried
612 

Length

Max length9
Median length8
Mean length8.121621622
Min length8

Characters and Unicode

Total characters40868
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnmarried
2nd rowUnmarried
3rd rowUnmarried
4th rowUnmarried
5th rowUnmarried

Common Values

ValueCountFrequency (%)
Married 4420
87.8%
Unmarried612
 
12.2%

Length

2023-03-19T20:28:57.861835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-19T20:28:57.995322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
married4420
87.8%
unmarried612
 
12.2%

Most occurring characters

ValueCountFrequency (%)
r10064
24.6%
a5032
12.3%
i5032
12.3%
e5032
12.3%
d5032
12.3%
M4420
10.8%
4420
10.8%
U612
 
1.5%
n612
 
1.5%
m612
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31416
76.9%
Uppercase Letter5032
 
12.3%
Space Separator4420
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r10064
32.0%
a5032
16.0%
i5032
16.0%
e5032
16.0%
d5032
16.0%
n612
 
1.9%
m612
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
M4420
87.8%
U612
 
12.2%
Space Separator
ValueCountFrequency (%)
4420
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin36448
89.2%
Common4420
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r10064
27.6%
a5032
13.8%
i5032
13.8%
e5032
13.8%
d5032
13.8%
M4420
12.1%
U612
 
1.7%
n612
 
1.7%
m612
 
1.7%
Common
ValueCountFrequency (%)
4420
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII40868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r10064
24.6%
a5032
12.3%
i5032
12.3%
e5032
12.3%
d5032
12.3%
M4420
10.8%
4420
10.8%
U612
 
1.5%
n612
 
1.5%
m612
 
1.5%

Nominee
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
Spouse
2447 
Brother
1258 
Sister
517 
Mother
407 
Father
403 

Length

Max length7
Median length6
Mean length6.25
Min length6

Characters and Unicode

Total characters31450
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFather
2nd rowSister
3rd rowFather
4th rowBrother
5th rowBrother

Common Values

ValueCountFrequency (%)
Spouse2447
48.6%
Brother1258
25.0%
Sister517
 
10.3%
Mother407
 
8.1%
Father403
 
8.0%

Length

2023-03-19T20:28:58.112119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-19T20:28:58.259872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
spouse2447
48.6%
brother1258
25.0%
sister517
 
10.3%
mother407
 
8.1%
father403
 
8.0%

Most occurring characters

ValueCountFrequency (%)
e5032
16.0%
o4112
13.1%
r3843
12.2%
S2964
9.4%
s2964
9.4%
t2585
8.2%
p2447
7.8%
u2447
7.8%
h2068
6.6%
B1258
 
4.0%
Other values (4)1730
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26418
84.0%
Uppercase Letter5032
 
16.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5032
19.0%
o4112
15.6%
r3843
14.5%
s2964
11.2%
t2585
9.8%
p2447
9.3%
u2447
9.3%
h2068
7.8%
i517
 
2.0%
a403
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
S2964
58.9%
B1258
25.0%
M407
 
8.1%
F403
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin31450
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5032
16.0%
o4112
13.1%
r3843
12.2%
S2964
9.4%
s2964
9.4%
t2585
8.2%
p2447
7.8%
u2447
7.8%
h2068
6.6%
B1258
 
4.0%
Other values (4)1730
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII31450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e5032
16.0%
o4112
13.1%
r3843
12.2%
S2964
9.4%
s2964
9.4%
t2585
8.2%
p2447
7.8%
u2447
7.8%
h2068
6.6%
B1258
 
4.0%
Other values (4)1730
 
5.5%

Dependents
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
3
1312 
2
1294 
4
1219 
1
1207 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5032
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
31312
26.1%
21294
25.7%
41219
24.2%
11207
24.0%

Length

2023-03-19T20:28:58.400868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-19T20:28:58.529958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
31312
26.1%
21294
25.7%
41219
24.2%
11207
24.0%

Most occurring characters

ValueCountFrequency (%)
31312
26.1%
21294
25.7%
41219
24.2%
11207
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5032
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
31312
26.1%
21294
25.7%
41219
24.2%
11207
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common5032
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
31312
26.1%
21294
25.7%
41219
24.2%
11207
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31312
26.1%
21294
25.7%
41219
24.2%
11207
24.0%

Accomodation
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
Rented
4962 
Own
 
70

Length

Max length6
Median length6
Mean length5.958267091
Min length3

Characters and Unicode

Total characters29982
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOwn
2nd rowRented
3rd rowOwn
4th rowRented
5th rowOwn

Common Values

ValueCountFrequency (%)
Rented4962
98.6%
Own70
 
1.4%

Length

2023-03-19T20:28:58.615226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-19T20:28:58.747956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
rented4962
98.6%
own70
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e9924
33.1%
n5032
16.8%
R4962
16.5%
t4962
16.5%
d4962
16.5%
O70
 
0.2%
w70
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24950
83.2%
Uppercase Letter5032
 
16.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e9924
39.8%
n5032
20.2%
t4962
19.9%
d4962
19.9%
w70
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
R4962
98.6%
O70
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin29982
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e9924
33.1%
n5032
16.8%
R4962
16.5%
t4962
16.5%
d4962
16.5%
O70
 
0.2%
w70
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII29982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e9924
33.1%
n5032
16.8%
R4962
16.5%
t4962
16.5%
d4962
16.5%
O70
 
0.2%
w70
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
Urban
3070 
Rural
1962 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters25160
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRural
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban3070
61.0%
Rural1962
39.0%

Length

2023-03-19T20:28:58.864485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-19T20:28:58.995974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban3070
61.0%
rural1962
39.0%

Most occurring characters

ValueCountFrequency (%)
r5032
20.0%
a5032
20.0%
U3070
12.2%
b3070
12.2%
n3070
12.2%
R1962
 
7.8%
u1962
 
7.8%
l1962
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20128
80.0%
Uppercase Letter5032
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r5032
25.0%
a5032
25.0%
b3070
15.3%
n3070
15.3%
u1962
 
9.7%
l1962
 
9.7%
Uppercase Letter
ValueCountFrequency (%)
U3070
61.0%
R1962
39.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25160
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r5032
20.0%
a5032
20.0%
U3070
12.2%
b3070
12.2%
n3070
12.2%
R1962
 
7.8%
u1962
 
7.8%
l1962
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII25160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r5032
20.0%
a5032
20.0%
U3070
12.2%
b3070
12.2%
n3070
12.2%
R1962
 
7.8%
u1962
 
7.8%
l1962
 
7.8%

PlanName
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct5032
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
CHSBC-01
 
1
CHSBC-3352
 
1
CHSBC-3359
 
1
CHSBC-3358
 
1
CHSBC-3357
 
1
Other values (5027)
5027 

Length

Max length10
Median length10
Mean length9.781796502
Min length8

Characters and Unicode

Total characters49222
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5032 ?
Unique (%)100.0%

Sample

1st rowCHSBC-01
2nd rowCHSBC-02
3rd rowCHSBC-03
4th rowCHSBC-04
5th rowCHSBC-05

Common Values

ValueCountFrequency (%)
CHSBC-011
 
< 0.1%
CHSBC-33521
 
< 0.1%
CHSBC-33591
 
< 0.1%
CHSBC-33581
 
< 0.1%
CHSBC-33571
 
< 0.1%
CHSBC-33561
 
< 0.1%
CHSBC-33551
 
< 0.1%
CHSBC-33541
 
< 0.1%
CHSBC-33531
 
< 0.1%
CHSBC-33511
 
< 0.1%
Other values (5022)5022
99.8%

Length

2023-03-19T20:28:59.085901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chsbc-011
 
< 0.1%
chsbc-131
 
< 0.1%
chsbc-061
 
< 0.1%
chsbc-071
 
< 0.1%
chsbc-081
 
< 0.1%
chsbc-091
 
< 0.1%
chsbc-381
 
< 0.1%
chsbc-101
 
< 0.1%
chsbc-121
 
< 0.1%
chsbc-141
 
< 0.1%
Other values (5022)5022
99.8%

Most occurring characters

ValueCountFrequency (%)
C10064
20.4%
H5032
10.2%
S5032
10.2%
B5032
10.2%
-5032
10.2%
12514
 
5.1%
22514
 
5.1%
32506
 
5.1%
42503
 
5.1%
51536
 
3.1%
Other values (5)7457
15.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter25160
51.1%
Decimal Number19030
38.7%
Dash Punctuation5032
 
10.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12514
13.2%
22514
13.2%
32506
13.2%
42503
13.2%
51536
8.1%
91503
7.9%
81503
7.9%
71503
7.9%
61503
7.9%
01445
7.6%
Uppercase Letter
ValueCountFrequency (%)
C10064
40.0%
H5032
20.0%
S5032
20.0%
B5032
20.0%
Dash Punctuation
ValueCountFrequency (%)
-5032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25160
51.1%
Common24062
48.9%

Most frequent character per script

Common
ValueCountFrequency (%)
-5032
20.9%
12514
10.4%
22514
10.4%
32506
10.4%
42503
10.4%
51536
 
6.4%
91503
 
6.2%
81503
 
6.2%
71503
 
6.2%
61503
 
6.2%
Latin
ValueCountFrequency (%)
C10064
40.0%
H5032
20.0%
S5032
20.0%
B5032
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII49222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C10064
20.4%
H5032
10.2%
S5032
10.2%
B5032
10.2%
-5032
10.2%
12514
 
5.1%
22514
 
5.1%
32506
 
5.1%
42503
 
5.1%
51536
 
3.1%
Other values (5)7457
15.1%

Planenrolledyear
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.544714
Minimum2010
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:28:59.207678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2013
Q12015
median2016
Q32019
95-th percentile2020
Maximum2021
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.601160979
Coefficient of variation (CV)0.001289909895
Kurtosis-0.3269901298
Mean2016.544714
Median Absolute Deviation (MAD)2
Skewness-0.3474071174
Sum10147253
Variance6.766038439
MonotonicityNot monotonic
2023-03-19T20:28:59.282421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
20191609
32.0%
20151348
26.8%
2014698
13.9%
2016402
 
8.0%
2017308
 
6.1%
2010211
 
4.2%
2021208
 
4.1%
2020184
 
3.7%
201116
 
0.3%
201216
 
0.3%
Other values (2)32
 
0.6%
ValueCountFrequency (%)
2010211
 
4.2%
201116
 
0.3%
201216
 
0.3%
201316
 
0.3%
2014698
13.9%
20151348
26.8%
2016402
 
8.0%
2017308
 
6.1%
201816
 
0.3%
20191609
32.0%
ValueCountFrequency (%)
2021208
 
4.1%
2020184
 
3.7%
20191609
32.0%
201816
 
0.3%
2017308
 
6.1%
2016402
 
8.0%
20151348
26.8%
2014698
13.9%
201316
 
0.3%
201216
 
0.3%

PolicyTerminyears
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.08465819
Minimum10
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:28:59.379653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q115
median18
Q320
95-th percentile25
Maximum30
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.432311556
Coefficient of variation (CV)0.2450868305
Kurtosis-0.6344702702
Mean18.08465819
Median Absolute Deviation (MAD)3
Skewness0.07028843539
Sum91002
Variance19.64538573
MonotonicityNot monotonic
2023-03-19T20:28:59.471420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
151532
30.4%
201001
19.9%
251000
19.9%
18999
19.9%
10499
 
9.9%
301
 
< 0.1%
ValueCountFrequency (%)
10499
 
9.9%
151532
30.4%
18999
19.9%
201001
19.9%
251000
19.9%
301
 
< 0.1%
ValueCountFrequency (%)
301
 
< 0.1%
251000
19.9%
201001
19.9%
18999
19.9%
151532
30.4%
10499
 
9.9%

Sumassured
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1808465.819
Minimum1000000
Maximum3000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:28:59.792387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile1000000
Q11500000
median1800000
Q32000000
95-th percentile2500000
Maximum3000000
Range2000000
Interquartile range (IQR)500000

Descriptive statistics

Standard deviation443231.1556
Coefficient of variation (CV)0.2450868305
Kurtosis-0.6344702702
Mean1808465.819
Median Absolute Deviation (MAD)300000
Skewness0.07028843539
Sum9100200000
Variance1.964538573 × 1011
MonotonicityNot monotonic
2023-03-19T20:28:59.933404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
15000001532
30.4%
20000001001
19.9%
25000001000
19.9%
1800000999
19.9%
1000000499
 
9.9%
30000001
 
< 0.1%
ValueCountFrequency (%)
1000000499
 
9.9%
15000001532
30.4%
1800000999
19.9%
20000001001
19.9%
25000001000
19.9%
30000001
 
< 0.1%
ValueCountFrequency (%)
30000001
 
< 0.1%
25000001000
19.9%
20000001001
19.9%
1800000999
19.9%
15000001532
30.4%
1000000499
 
9.9%

YearofMaturity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2034.629372
Minimum2024
Maximum2046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:29:00.080947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2024
5-th percentile2025
Q12031.75
median2034
Q32039
95-th percentile2044
Maximum2046
Range22
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation5.287928723
Coefficient of variation (CV)0.002598964114
Kurtosis-0.4242265311
Mean2034.629372
Median Absolute Deviation (MAD)3
Skewness-0.08833457757
Sum10238255
Variance27.96219018
MonotonicityNot monotonic
2023-03-19T20:29:00.228128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2034742
14.7%
2039564
11.2%
2040389
7.7%
2031386
7.7%
2025381
7.6%
2037380
7.6%
2044365
7.3%
2033361
7.2%
2035359
7.1%
2032339
6.7%
Other values (9)766
15.2%
ValueCountFrequency (%)
2024102
 
2.0%
2025381
7.6%
202624
 
0.5%
2027179
 
3.6%
2030186
 
3.7%
2031386
7.7%
2032339
6.7%
2033361
7.2%
2034742
14.7%
2035359
7.1%
ValueCountFrequency (%)
204642
 
0.8%
204543
 
0.9%
2044365
7.3%
204146
 
0.9%
2040389
7.7%
2039564
11.2%
203856
 
1.1%
2037380
7.6%
203688
 
1.7%
2035359
7.1%

YearlyPremium
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180846.5819
Minimum100000
Maximum300000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:29:00.353368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum100000
5-th percentile100000
Q1150000
median180000
Q3200000
95-th percentile250000
Maximum300000
Range200000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation44323.11556
Coefficient of variation (CV)0.2450868305
Kurtosis-0.6344702702
Mean180846.5819
Median Absolute Deviation (MAD)30000
Skewness0.07028843539
Sum910020000
Variance1964538573
MonotonicityNot monotonic
2023-03-19T20:29:00.430660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1500001532
30.4%
2000001001
19.9%
2500001000
19.9%
180000999
19.9%
100000499
 
9.9%
3000001
 
< 0.1%
ValueCountFrequency (%)
100000499
 
9.9%
1500001532
30.4%
180000999
19.9%
2000001001
19.9%
2500001000
19.9%
3000001
 
< 0.1%
ValueCountFrequency (%)
3000001
 
< 0.1%
2500001000
19.9%
2000001001
19.9%
180000999
19.9%
1500001532
30.4%
100000499
 
9.9%

MonthlyPremium
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15070.54849
Minimum8333.333333
Maximum25000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:29:00.548176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8333.333333
5-th percentile8333.333333
Q112500
median15000
Q316666.66667
95-th percentile20833.33333
Maximum25000
Range16666.66667
Interquartile range (IQR)4166.666667

Descriptive statistics

Standard deviation3693.592963
Coefficient of variation (CV)0.2450868305
Kurtosis-0.6344702702
Mean15070.54849
Median Absolute Deviation (MAD)2500
Skewness0.07028843539
Sum75835000
Variance13642628.98
MonotonicityNot monotonic
2023-03-19T20:29:00.634463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
125001532
30.4%
16666.666671001
19.9%
20833.333331000
19.9%
15000999
19.9%
8333.333333499
 
9.9%
250001
 
< 0.1%
ValueCountFrequency (%)
8333.333333499
 
9.9%
125001532
30.4%
15000999
19.9%
16666.666671001
19.9%
20833.333331000
19.9%
250001
 
< 0.1%
ValueCountFrequency (%)
250001
 
< 0.1%
20833.333331000
19.9%
16666.666671001
19.9%
15000999
19.9%
125001532
30.4%
8333.333333499
 
9.9%

NumberofPremiumspaidinyears
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.455286169
Minimum2
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:29:00.744569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median7
Q38
95-th percentile10
Maximum13
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.601160979
Coefficient of variation (CV)0.4029505294
Kurtosis-0.3269901298
Mean6.455286169
Median Absolute Deviation (MAD)2
Skewness0.3474071174
Sum32483
Variance6.766038439
MonotonicityNot monotonic
2023-03-19T20:29:00.854274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
41609
32.0%
81348
26.8%
9698
13.9%
7402
 
8.0%
6308
 
6.1%
13211
 
4.2%
2208
 
4.1%
3184
 
3.7%
1216
 
0.3%
1116
 
0.3%
Other values (2)32
 
0.6%
ValueCountFrequency (%)
2208
 
4.1%
3184
 
3.7%
41609
32.0%
516
 
0.3%
6308
 
6.1%
7402
 
8.0%
81348
26.8%
9698
13.9%
1016
 
0.3%
1116
 
0.3%
ValueCountFrequency (%)
13211
 
4.2%
1216
 
0.3%
1116
 
0.3%
1016
 
0.3%
9698
13.9%
81348
26.8%
7402
 
8.0%
6308
 
6.1%
516
 
0.3%
41609
32.0%

NumberofPremiumspaidtilldateinmonths
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.46343402
Minimum24
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:29:00.939287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile36
Q148
median84
Q396
95-th percentile120
Maximum156
Range132
Interquartile range (IQR)48

Descriptive statistics

Standard deviation31.21393175
Coefficient of variation (CV)0.4029505294
Kurtosis-0.3269901298
Mean77.46343402
Median Absolute Deviation (MAD)24
Skewness0.3474071174
Sum389796
Variance974.3095353
MonotonicityNot monotonic
2023-03-19T20:29:01.030615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
481609
32.0%
961348
26.8%
108698
13.9%
84402
 
8.0%
72308
 
6.1%
156211
 
4.2%
24208
 
4.1%
36184
 
3.7%
14416
 
0.3%
13216
 
0.3%
Other values (2)32
 
0.6%
ValueCountFrequency (%)
24208
 
4.1%
36184
 
3.7%
481609
32.0%
6016
 
0.3%
72308
 
6.1%
84402
 
8.0%
961348
26.8%
108698
13.9%
12016
 
0.3%
13216
 
0.3%
ValueCountFrequency (%)
156211
 
4.2%
14416
 
0.3%
13216
 
0.3%
12016
 
0.3%
108698
13.9%
961348
26.8%
84402
 
8.0%
72308
 
6.1%
6016
 
0.3%
481609
32.0%

PremiumPaidtilldate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96638.67912
Minimum16666.66667
Maximum200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:29:01.194762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16666.66667
5-th percentile41666.66667
Q160000
median83333.33333
Q3133333.3333
95-th percentile166666.6667
Maximum200000
Range183333.3333
Interquartile range (IQR)73333.33333

Descriptive statistics

Standard deviation45196.65902
Coefficient of variation (CV)0.4676870527
Kurtosis-0.9764247095
Mean96638.67912
Median Absolute Deviation (MAD)33333.33333
Skewness0.4942840022
Sum486285833.3
Variance2042737986
MonotonicityNot monotonic
2023-03-19T20:29:01.306728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
50000789
15.7%
66666.66667486
 
9.7%
83333.33333365
 
7.3%
60000364
 
7.2%
87500346
 
6.9%
120000345
 
6.9%
166666.6667343
 
6.8%
133333.3333303
 
6.0%
75000215
 
4.3%
162500211
 
4.2%
Other values (18)1265
25.1%
ValueCountFrequency (%)
16666.6666724
 
0.5%
2500056
 
1.1%
3000040
 
0.8%
33333.3333346
 
0.9%
3750056
 
1.1%
41666.6666742
 
0.8%
4500040
 
0.8%
50000789
15.7%
58333.3333324
 
0.5%
60000364
7.2%
ValueCountFrequency (%)
20000017
 
0.3%
187500208
4.1%
183333.333316
 
0.3%
166666.666716
 
0.3%
166666.6667343
6.8%
162500211
4.2%
150000178
3.5%
135000210
4.2%
133333.3333303
6.0%
120000345
6.9%

latepayment0to3months
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
0
3592 
2
902 
1
492 
3
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5032
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
03592
71.4%
2902
 
17.9%
1492
 
9.8%
346
 
0.9%

Length

2023-03-19T20:29:01.450575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-19T20:29:01.565691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03592
71.4%
2902
 
17.9%
1492
 
9.8%
346
 
0.9%

Most occurring characters

ValueCountFrequency (%)
03592
71.4%
2902
 
17.9%
1492
 
9.8%
346
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5032
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03592
71.4%
2902
 
17.9%
1492
 
9.8%
346
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common5032
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03592
71.4%
2902
 
17.9%
1492
 
9.8%
346
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII5032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03592
71.4%
2902
 
17.9%
1492
 
9.8%
346
 
0.9%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
3
4979 
4
 
26
5
 
14
6
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5032
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
34979
98.9%
426
 
0.5%
514
 
0.3%
613
 
0.3%

Length

2023-03-19T20:29:01.686194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-19T20:29:01.806082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
34979
98.9%
426
 
0.5%
514
 
0.3%
613
 
0.3%

Most occurring characters

ValueCountFrequency (%)
34979
98.9%
426
 
0.5%
514
 
0.3%
613
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5032
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
34979
98.9%
426
 
0.5%
514
 
0.3%
613
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common5032
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
34979
98.9%
426
 
0.5%
514
 
0.3%
613
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34979
98.9%
426
 
0.5%
514
 
0.3%
613
 
0.3%

latepayment6to9months
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2442368839
Minimum0
Maximum9
Zeros4781
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:29:01.910411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.078035795
Coefficient of variation (CV)4.41389432
Kurtosis17.0195761
Mean0.2442368839
Median Absolute Deviation (MAD)0
Skewness4.292558595
Sum1229
Variance1.162161175
MonotonicityNot monotonic
2023-03-19T20:29:02.026396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
04781
95.0%
5216
 
4.3%
318
 
0.4%
49
 
0.2%
74
 
0.1%
82
 
< 0.1%
61
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
04781
95.0%
318
 
0.4%
49
 
0.2%
5216
 
4.3%
61
 
< 0.1%
74
 
0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
82
 
< 0.1%
74
 
0.1%
61
 
< 0.1%
5216
 
4.3%
49
 
0.2%
318
 
0.4%
04781
95.0%

latepayment9to12months
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.226550079
Minimum4
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:29:02.116753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median4
Q34
95-th percentile4
Maximum12
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.058958894
Coefficient of variation (CV)0.2505492362
Kurtosis19.89288922
Mean4.226550079
Median Absolute Deviation (MAD)0
Skewness4.624771724
Sum21268
Variance1.121393939
MonotonicityNot monotonic
2023-03-19T20:29:02.203977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
44788
95.2%
9167
 
3.3%
1038
 
0.8%
533
 
0.7%
114
 
0.1%
122
 
< 0.1%
ValueCountFrequency (%)
44788
95.2%
533
 
0.7%
9167
 
3.3%
1038
 
0.8%
114
 
0.1%
122
 
< 0.1%
ValueCountFrequency (%)
122
 
< 0.1%
114
 
0.1%
1038
 
0.8%
9167
 
3.3%
533
 
0.7%
44788
95.2%

Morethan12monthsdelay
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.071343402
Minimum0
Maximum10
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:29:02.334980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median4
Q34
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5348866242
Coefficient of variation (CV)0.1313784104
Kurtosis80.03529924
Mean4.071343402
Median Absolute Deviation (MAD)0
Skewness7.329881221
Sum20487
Variance0.2861037008
MonotonicityNot monotonic
2023-03-19T20:29:02.411645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
44738
94.2%
5184
 
3.7%
642
 
0.8%
230
 
0.6%
1026
 
0.5%
39
 
0.2%
82
 
< 0.1%
01
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
230
 
0.6%
39
 
0.2%
44738
94.2%
5184
 
3.7%
642
 
0.8%
82
 
< 0.1%
1026
 
0.5%
ValueCountFrequency (%)
1026
 
0.5%
82
 
< 0.1%
642
 
0.8%
5184
 
3.7%
44738
94.2%
39
 
0.2%
230
 
0.6%
01
 
< 0.1%

Totaldelayedmonths
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.04431638
Minimum9
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:29:02.489095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile11
Q111
median11
Q313
95-th percentile16
Maximum31
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.032880066
Coefficient of variation (CV)0.1687833499
Kurtosis13.04963363
Mean12.04431638
Median Absolute Deviation (MAD)0
Skewness3.058634527
Sum60607
Variance4.132601361
MonotonicityNot monotonic
2023-03-19T20:29:02.587970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
113148
62.6%
13764
 
15.2%
12543
 
10.8%
16219
 
4.4%
1882
 
1.6%
1480
 
1.6%
1776
 
1.5%
1929
 
0.6%
922
 
0.4%
1520
 
0.4%
Other values (12)49
 
1.0%
ValueCountFrequency (%)
922
 
0.4%
106
 
0.1%
113148
62.6%
12543
 
10.8%
13764
 
15.2%
1480
 
1.6%
1520
 
0.4%
16219
 
4.4%
1776
 
1.5%
1882
 
1.6%
ValueCountFrequency (%)
311
 
< 0.1%
291
 
< 0.1%
282
 
< 0.1%
272
 
< 0.1%
261
 
< 0.1%
2512
0.2%
243
 
0.1%
2310
0.2%
222
 
< 0.1%
218
0.2%

RiskScore
Real number (ℝ≥0)

HIGH CORRELATION

Distinct97
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1869644737
Minimum0.05769230769
Maximum1.041666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-03-19T20:29:02.688682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.05769230769
5-th percentile0.1066558442
Q10.1203703704
median0.1527777778
Q30.2291666667
95-th percentile0.375
Maximum1.041666667
Range0.983974359
Interquartile range (IQR)0.1087962963

Descriptive statistics

Standard deviation0.0955737848
Coefficient of variation (CV)0.5111868737
Kurtosis5.976640988
Mean0.1869644737
Median Absolute Deviation (MAD)0.03819444444
Skewness1.898502381
Sum940.8052316
Variance0.009134348341
MonotonicityNot monotonic
2023-03-19T20:29:02.838009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.22916666671177
23.4%
0.1145833333980
19.5%
0.1203703704605
12.0%
0.130952381282
 
5.6%
0.1527777778228
 
4.5%
0.25193
 
3.8%
0.125178
 
3.5%
0.1666666667164
 
3.3%
0.4583333333159
 
3.2%
0.07051282051141
 
2.8%
Other values (87)925
18.4%
ValueCountFrequency (%)
0.057692307693
 
0.1%
0.07051282051141
2.8%
0.0763888888913
 
0.3%
0.0769230769223
 
0.5%
0.0833333333328
 
0.6%
0.089743589746
 
0.1%
0.090909090912
 
< 0.1%
0.0916666666712
 
0.2%
0.093752
 
< 0.1%
0.098484848482
 
< 0.1%
ValueCountFrequency (%)
1.0416666672
 
< 0.1%
0.751
 
< 0.1%
0.70833333337
 
0.1%
0.66666666678
 
0.2%
0.6251
 
< 0.1%
0.58333333331
 
< 0.1%
0.54166666673
 
0.1%
0.52777777783
 
0.1%
0.52083333334
 
0.1%
0.527
0.5%

Default
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
1
2983 
0
2049 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5032
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12983
59.3%
02049
40.7%

Length

2023-03-19T20:29:03.006687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-19T20:29:03.159830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
12983
59.3%
02049
40.7%

Most occurring characters

ValueCountFrequency (%)
12983
59.3%
02049
40.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5032
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12983
59.3%
02049
40.7%

Most occurring scripts

ValueCountFrequency (%)
Common5032
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12983
59.3%
02049
40.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12983
59.3%
02049
40.7%

Interactions

2023-03-19T20:28:52.998799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:07.365766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:10.537539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:13.147611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:15.459309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:18.034563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:20.424055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:23.109935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:25.266178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:28.621764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:32.743666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:35.710343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:40.494304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:43.382469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:45.778535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:48.155702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:50.766999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:53.121731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:07.645171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:10.698330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:13.280112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:15.643969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:18.134732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:20.544516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:23.241466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:25.592168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-03-19T20:28:12.493810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:14.936208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:17.480271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:19.923087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:22.459354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:24.774795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:27.713229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:31.662701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:35.148039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:39.279582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:42.799252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:45.104896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:47.657054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:50.071876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:52.514539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:54.824423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:10.180558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:12.666657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:15.068213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:17.588579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:20.067681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:22.643511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:24.892921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:27.902127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:31.866866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:35.304382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:39.535950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:42.956562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:45.238504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:47.799629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:50.208291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:52.634793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:54.917996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:10.282693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:12.851570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:15.184116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:17.778597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:20.194875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:22.807662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:25.019792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:28.110816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:32.052583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:35.438824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:39.771065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:43.095765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:45.357713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:47.937147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:50.534193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:52.782321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:55.229811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:10.429775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:13.004524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:15.306655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:17.893892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:20.315999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:22.958523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:25.148860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:28.334245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:32.261400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:35.571433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:40.243994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:43.266132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:45.669034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:48.042151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:50.667322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:28:52.882915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-19T20:29:03.473912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:29:03.831361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:29:04.185684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:29:04.453322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-19T20:29:04.686606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

2023-03-19T20:28:55.484466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-19T20:28:56.403497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

PolicyholderIDPolicyholderNamePolicyholderAgeinyearsIncomeofPolicyholderperannumMaritalStatusNomineeDependentsAccomodationResidenceAreaTypePlanNamePlanenrolledyearPolicyTerminyearsSumassuredYearofMaturityYearlyPremiumMonthlyPremiumNumberofPremiumspaidinyearsNumberofPremiumspaidtilldateinmonthsPremiumPaidtilldatelatepayment0to3monthslatepayment3to6monthslatepayment6to9monthslatepayment9to12monthsMorethan12monthsdelayTotaldelayedmonthsRiskScoreDefault
01Sarat Sharma35.0000001747520UnmarriedFather3OwnRuralCHSBC-01202110100000020311000008333.33333322416666.66666703044110.4583330
12Sandeep Balasubramanian35.000000750060UnmarriedSister1RentedUrbanCHSBC-02201610100000020261000008333.33333378458333.33333313044120.1428571
23Prem Kumar30.032877852060UnmarriedFather1OwnUrbanCHSBC-03201510100000020251000008333.33333389666666.66666703044110.1145831
34Sajal Toshniwal30.035616750070UnmarriedBrother1RentedUrbanCHSBC-04201710100000020271000008333.33333367250000.00000003045120.1666671
45Chandrakumar30.0410961125120UnmarriedBrother1OwnUrbanCHSBC-05201410100000020241000008333.333333910875000.00000023044130.1203701
56Charan Kumar31.013699750130UnmarriedSister4RentedRuralCHSBC-06201510100000020251000008333.33333389666666.66666703044110.1145831
67Prabhudev31.0383561492580UnmarriedMother4OwnRuralCHSBC-07201710100000020271000008333.33333367250000.00000003044110.1527781
78Kaviselvan Panneerselvam32.032877900080UnmarriedSister2OwnUrbanCHSBC-08202110100000020311000008333.33333322416666.66666703044110.4583330
89Siva Priyan33.0301371290140UnmarriedFather4OwnUrbanCHSBC-09201610100000020261000008333.33333378458333.33333313044120.1428571
910Anandh34.0246581185140UnmarriedMother3OwnRuralCHSBC-10201510100000020251000008333.33333389666666.66666703044110.1145831

Last rows

PolicyholderIDPolicyholderNamePolicyholderAgeinyearsIncomeofPolicyholderperannumMaritalStatusNomineeDependentsAccomodationResidenceAreaTypePlanNamePlanenrolledyearPolicyTerminyearsSumassuredYearofMaturityYearlyPremiumMonthlyPremiumNumberofPremiumspaidinyearsNumberofPremiumspaidtilldateinmonthsPremiumPaidtilldatelatepayment0to3monthslatepayment3to6monthslatepayment6to9monthslatepayment9to12monthsMorethan12monthsdelayTotaldelayedmonthsRiskScoreDefault
50225024S N Gnana Giri43.019178226570MarriedFather1RentedUrbanCHSBC-50232019151500000203415000012500.044850000.003044110.2291670
50235025Ramya Sai Galla Venkat76.013699226440MarriedBrother4RentedUrbanCHSBC-50242017151500000203215000012500.067275000.003044110.1527781
50245026Pravin Terdale50.035616226360MarriedSpouse1RentedUrbanCHSBC-50252015151500000203015000012500.0896100000.003044110.1145831
50255027Subha Selvam31.024658226860UnmarriedSister3RentedRuralCHSBC-50262019151500000203415000012500.044850000.003044110.2291670
50265028Deepika Yash Chopra47.035616226580MarriedSpouse2RentedRuralCHSBC-50272017151500000203215000012500.067275000.013044120.1666671
50275029Narayanan Rajulu43.027397226650MarriedSister1RentedRuralCHSBC-50282015151500000203015000012500.0896100000.003044110.1145831
50285030Samy Babu35.000000226720UnmarriedFather1RentedRuralCHSBC-50292019151500000203415000012500.044850000.003044110.2291670
50295031Sanhas Allekat74.041096226440MarriedBrother2RentedUrbanCHSBC-50302015151500000203015000012500.0896100000.013044120.1250001
50305032Subha Shree Nagarajan62.035616226570MarriedSpouse1RentedRuralCHSBC-50312019151500000203415000012500.044850000.003044110.2291670
50315033Martin Thomas35.000000226360UnmarriedMother4RentedUrbanCHSBC-50322015151500000203015000012500.0896100000.003044110.1145831